Conventional computer-based search, in general, is extremely text-centric in that search engines typically analyze alphanumeric search queries in order to return results. To the extent visualization is incorporated into a search, it is often performed through use of metadata, for example, where items are manually pre-tagged with metadata corresponding to physical attributes of the visual item. In other words, traditional search engines employ pre-indexed metadata in order to return image data in response to a search query.
To maximize likelihood of locating relevant information amongst an abundance of data, search engines are often employed over the web or a subset of pages thereof. A search engine is a tool that facilitates web navigation based on entry of a search query comprising one or more keywords. Upon receipt of a query, the search engine retrieves a list of websites, typically ranked based on relevance to the query. To enable this functionality, the search engine must generate and maintain a supporting infrastructure.
Upon entry of one or more keywords as a search query, the search engine retrieves information that matches the query from an index, ranks the sites that match the query, generates a snippet of text associated with matching sites and displays the results to a user. Furthermore, advertisements relating to the search terms can also be displayed together with the results. The user can thereafter scroll through a plurality of returned sites, ads, and the like in an attempt to identify information of interest. However, this can be an extremely time-consuming and frustrating process as search engines can return a substantial number of sites. Many times, the user is forced to narrow the search iteratively by altering and/or adding keywords and operators to obtain the identity of websites including relevant information.